与计算机学习增强的经手术期结局风险评估协议 (Periop ORACLE) 随机研究
Bradley Fritz1, Christopher King1, Yixin Chen2
1Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, 63110, USA.
F1000Research
|August 7, 2023
概括
使用机器学习工具的麻醉学临床医生可以比没有人工智能的临床医生更准确地预测术后并发症,例如死亡和急性损伤. 这项研究探讨了人工智能.
科学领域:
- 麻醉学和外科手术期间的医学.
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 每年都有数以百万计的死亡和急性损伤 (AKI) 病例发生在手术后.
- 早期风险识别和手术内减轻风险对于预防不良结果至关重要.
- 术内远程医疗在麻醉学中未得到充分利用,尽管它在重症监护中取得了成功.
研究的目的:
- 评价麻醉学临床医生在预测手术后并发症的准确性,有或没有机器学习 (ML) 辅助.
- 探索ML工具在利用电子健康数据进行术后风险评估方面的潜力.
- 定义ML在手术内远程医疗和更广泛的外科风险评估中的作用.
主要方法:
- 在TECTONICS随机临床试验 (NCT03923699) 中嵌入的一个子研究.
- 在远程医疗环境中,麻醉学临床医生审查了手术病例,评估了30天住院死亡或AKI的可能性.
- 案例审查被随机分类,以包括对ML预测的访问或不包括,并将预测准确性与组之间进行比较.
主要成果:
- 这项研究旨在比较ML辅助预测与标准临床判断的准确性.
- 结果将量化ML整合后预测术后并发症 (死亡,AKI) 的改进.
- 这将提供有关ML工具在手术期间实时临床决策中的有效性的数据.
结论:
- 这项研究将阐明机器学习在提高麻醉学临床医生风险预测准确性的价值.
- 这些发现将有助于将基于ML的远程医疗整合到外科手术工作流程中.
- 成功的结果将支持ML在外科手术期间风险评估和缓解方面的更广泛应用.
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